Overview

Dataset statistics

Number of variables14
Number of observations106594
Missing cells106594
Missing cells (%)7.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory18.0 MiB
Average record size in memory177.0 B

Variable types

DateTime1
Numeric11
Categorical1
Unsupported1

Alerts

ghi_pyr is highly overall correlated with ghi_rsi and 3 other fieldsHigh correlation
ghi_rsi is highly overall correlated with ghi_pyr and 2 other fieldsHigh correlation
dni is highly overall correlated with ghi_pyr and 2 other fieldsHigh correlation
dhi is highly overall correlated with ghi_pyr and 2 other fieldsHigh correlation
air_temperature is highly overall correlated with ghi_pyr and 2 other fieldsHigh correlation
wind_speed is highly overall correlated with wind_speed_of_gust and 1 other fieldsHigh correlation
wind_speed_of_gust is highly overall correlated with air_temperature and 2 other fieldsHigh correlation
barometric_pressure is highly overall correlated with air_temperature and 2 other fieldsHigh correlation
sensor_cleaning is highly imbalanced (99.0%)Imbalance
comments has 106594 (100.0%) missing valuesMissing
time has unique valuesUnique
comments is an unsupported type, check if it needs cleaning or further analysisUnsupported
ghi_pyr has 51912 (48.7%) zerosZeros
ghi_rsi has 46524 (43.6%) zerosZeros
dni has 58707 (55.1%) zerosZeros
dhi has 46524 (43.6%) zerosZeros

Reproduction

Analysis started2023-07-13 14:25:40.058522
Analysis finished2023-07-13 14:26:15.390191
Duration35.33 seconds
Software versionydata-profiling vv4.3.1
Download configurationconfig.json

Variables

time
Date

UNIQUE 

Distinct106594
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size832.9 KiB
Minimum2015-04-21 18:30:00
Maximum2017-05-01 00:00:00
2023-07-13T19:26:15.656101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-13T19:26:16.088381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

ghi_pyr
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct10332
Distinct (%)9.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean249.03033
Minimum0
Maximum1151.2
Zeros51912
Zeros (%)48.7%
Negative0
Negative (%)0.0%
Memory size832.9 KiB
2023-07-13T19:26:16.428349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median4
Q3523.7
95-th percentile908.7
Maximum1151.2
Range1151.2
Interquartile range (IQR)523.7

Descriptive statistics

Standard deviation329.9626
Coefficient of variation (CV)1.3249896
Kurtosis-0.65099626
Mean249.03033
Median Absolute Deviation (MAD)4
Skewness0.9451745
Sum26545139
Variance108875.32
MonotonicityNot monotonic
2023-07-13T19:26:16.759994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 51912
48.7%
0.2 71
 
0.1%
0.3 63
 
0.1%
0.1 60
 
0.1%
1.1 60
 
0.1%
0.5 60
 
0.1%
0.4 55
 
0.1%
0.6 50
 
< 0.1%
1.2 48
 
< 0.1%
1.5 47
 
< 0.1%
Other values (10322) 54168
50.8%
ValueCountFrequency (%)
0 51912
48.7%
0.1 60
 
0.1%
0.2 71
 
0.1%
0.3 63
 
0.1%
0.4 55
 
0.1%
0.5 60
 
0.1%
0.6 50
 
< 0.1%
0.7 41
 
< 0.1%
0.8 43
 
< 0.1%
0.9 37
 
< 0.1%
ValueCountFrequency (%)
1151.2 1
< 0.1%
1126 1
< 0.1%
1122.4 1
< 0.1%
1116.2 1
< 0.1%
1113.7 1
< 0.1%
1108 1
< 0.1%
1106.8 1
< 0.1%
1096 1
< 0.1%
1092.9 1
< 0.1%
1091.2 1
< 0.1%

ghi_rsi
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct9918
Distinct (%)9.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean239.59901
Minimum0
Maximum1100.9
Zeros46524
Zeros (%)43.6%
Negative0
Negative (%)0.0%
Memory size832.9 KiB
2023-07-13T19:26:17.095899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median3.5
Q3505.1
95-th percentile868.5
Maximum1100.9
Range1100.9
Interquartile range (IQR)505.1

Descriptive statistics

Standard deviation316.63119
Coefficient of variation (CV)1.3215046
Kurtosis-0.68002644
Mean239.59901
Median Absolute Deviation (MAD)3.5
Skewness0.93467279
Sum25539817
Variance100255.31
MonotonicityNot monotonic
2023-07-13T19:26:17.463066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 46524
43.6%
0.1 1994
 
1.9%
0.2 1687
 
1.6%
0.3 623
 
0.6%
0.4 188
 
0.2%
0.5 186
 
0.2%
0.7 143
 
0.1%
0.6 136
 
0.1%
1.2 132
 
0.1%
1.1 116
 
0.1%
Other values (9908) 54865
51.5%
ValueCountFrequency (%)
0 46524
43.6%
0.1 1994
 
1.9%
0.2 1687
 
1.6%
0.3 623
 
0.6%
0.4 188
 
0.2%
0.5 186
 
0.2%
0.6 136
 
0.1%
0.7 143
 
0.1%
0.8 115
 
0.1%
0.9 110
 
0.1%
ValueCountFrequency (%)
1100.9 1
< 0.1%
1082.4 1
< 0.1%
1077.3 1
< 0.1%
1075.9 1
< 0.1%
1070.5 1
< 0.1%
1063.9 1
< 0.1%
1061.9 1
< 0.1%
1055.4 1
< 0.1%
1054.3 1
< 0.1%
1046.4 1
< 0.1%

dni
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct8960
Distinct (%)8.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean202.61395
Minimum0
Maximum993.9
Zeros58707
Zeros (%)55.1%
Negative0
Negative (%)0.0%
Memory size832.9 KiB
2023-07-13T19:26:17.810814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3443.2
95-th percentile736
Maximum993.9
Range993.9
Interquartile range (IQR)443.2

Descriptive statistics

Standard deviation275.36803
Coefficient of variation (CV)1.3590774
Kurtosis-0.65813467
Mean202.61395
Median Absolute Deviation (MAD)0
Skewness0.94488169
Sum21597431
Variance75827.551
MonotonicityNot monotonic
2023-07-13T19:26:18.159856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 58707
55.1%
3.1 23
 
< 0.1%
0.4 21
 
< 0.1%
3.4 19
 
< 0.1%
0.3 19
 
< 0.1%
0.2 18
 
< 0.1%
0.1 18
 
< 0.1%
2.5 17
 
< 0.1%
3.8 17
 
< 0.1%
670 17
 
< 0.1%
Other values (8950) 47718
44.8%
ValueCountFrequency (%)
0 58707
55.1%
0.1 18
 
< 0.1%
0.2 18
 
< 0.1%
0.3 19
 
< 0.1%
0.4 21
 
< 0.1%
0.5 9
 
< 0.1%
0.6 14
 
< 0.1%
0.7 15
 
< 0.1%
0.8 12
 
< 0.1%
0.9 9
 
< 0.1%
ValueCountFrequency (%)
993.9 1
< 0.1%
989.2 1
< 0.1%
986.5 1
< 0.1%
982.1 1
< 0.1%
981.2 1
< 0.1%
980.9 1
< 0.1%
980.5 1
< 0.1%
978.7 1
< 0.1%
977.8 1
< 0.1%
976.5 1
< 0.1%

dhi
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct5521
Distinct (%)5.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean100.45506
Minimum0
Maximum673
Zeros46524
Zeros (%)43.6%
Negative0
Negative (%)0.0%
Memory size832.9 KiB
2023-07-13T19:26:18.496266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median3.5
Q3188.1
95-th percentile360.5
Maximum673
Range673
Interquartile range (IQR)188.1

Descriptive statistics

Standard deviation130.82675
Coefficient of variation (CV)1.3023411
Kurtosis0.57838244
Mean100.45506
Median Absolute Deviation (MAD)3.5
Skewness1.1743655
Sum10707906
Variance17115.638
MonotonicityNot monotonic
2023-07-13T19:26:18.822060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 46524
43.6%
0.1 1994
 
1.9%
0.2 1687
 
1.6%
0.3 623
 
0.6%
0.4 188
 
0.2%
0.5 186
 
0.2%
0.7 143
 
0.1%
0.6 136
 
0.1%
1.2 132
 
0.1%
1.1 116
 
0.1%
Other values (5511) 54865
51.5%
ValueCountFrequency (%)
0 46524
43.6%
0.1 1994
 
1.9%
0.2 1687
 
1.6%
0.3 623
 
0.6%
0.4 188
 
0.2%
0.5 186
 
0.2%
0.6 136
 
0.1%
0.7 143
 
0.1%
0.8 115
 
0.1%
0.9 110
 
0.1%
ValueCountFrequency (%)
673 1
< 0.1%
672.5 1
< 0.1%
670 1
< 0.1%
665.2 1
< 0.1%
664.5 1
< 0.1%
657.1 1
< 0.1%
649.6 1
< 0.1%
649.3 1
< 0.1%
649.1 1
< 0.1%
647.4 1
< 0.1%

air_temperature
Real number (ℝ)

HIGH CORRELATION 

Distinct410
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27.692962
Minimum7.8
Maximum49.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size832.9 KiB
2023-07-13T19:26:19.182283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum7.8
5-th percentile15.6
Q123
median28.1
Q332.4
95-th percentile38.8
Maximum49.5
Range41.7
Interquartile range (IQR)9.4

Descriptive statistics

Standard deviation6.9149428
Coefficient of variation (CV)0.24970037
Kurtosis-0.38605333
Mean27.692962
Median Absolute Deviation (MAD)4.6
Skewness-0.12704659
Sum2951903.6
Variance47.816434
MonotonicityNot monotonic
2023-07-13T19:26:19.505453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
28.4 917
 
0.9%
28.9 906
 
0.8%
29.1 902
 
0.8%
28.6 875
 
0.8%
28.1 868
 
0.8%
27.9 865
 
0.8%
29.2 829
 
0.8%
29.4 827
 
0.8%
27.1 814
 
0.8%
26.9 810
 
0.8%
Other values (400) 97981
91.9%
ValueCountFrequency (%)
7.8 2
< 0.1%
7.9 1
 
< 0.1%
8 2
< 0.1%
8.1 1
 
< 0.1%
8.2 3
< 0.1%
8.3 1
 
< 0.1%
8.4 1
 
< 0.1%
8.5 1
 
< 0.1%
8.6 1
 
< 0.1%
8.7 2
< 0.1%
ValueCountFrequency (%)
49.5 1
 
< 0.1%
49 2
< 0.1%
48.9 3
< 0.1%
48.8 2
< 0.1%
48.7 3
< 0.1%
48.6 1
 
< 0.1%
48.5 2
< 0.1%
48.4 2
< 0.1%
48.3 1
 
< 0.1%
48.2 1
 
< 0.1%

relative_humidity
Real number (ℝ)

Distinct970
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean51.710956
Minimum2.7
Maximum99.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size832.9 KiB
2023-07-13T19:26:19.821109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.7
5-th percentile18.8
Q136
median50.8
Q369.2
95-th percentile83.2
Maximum99.9
Range97.2
Interquartile range (IQR)33.2

Descriptive statistics

Standard deviation20.344793
Coefficient of variation (CV)0.39343293
Kurtosis-0.86679879
Mean51.710956
Median Absolute Deviation (MAD)16.4
Skewness-0.0074660303
Sum5512077.6
Variance413.9106
MonotonicityNot monotonic
2023-07-13T19:26:20.234456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
36 232
 
0.2%
75.5 228
 
0.2%
74.5 222
 
0.2%
46.1 222
 
0.2%
46.5 220
 
0.2%
73.8 220
 
0.2%
76.8 219
 
0.2%
42.9 217
 
0.2%
77 216
 
0.2%
48 215
 
0.2%
Other values (960) 104383
97.9%
ValueCountFrequency (%)
2.7 2
 
< 0.1%
2.9 2
 
< 0.1%
3 1
 
< 0.1%
3.1 3
< 0.1%
3.2 2
 
< 0.1%
3.3 2
 
< 0.1%
3.5 4
< 0.1%
3.6 3
< 0.1%
3.7 5
< 0.1%
3.8 4
< 0.1%
ValueCountFrequency (%)
99.9 4
 
< 0.1%
99.8 3
 
< 0.1%
99.7 2
 
< 0.1%
99.6 2
 
< 0.1%
99.5 5
 
< 0.1%
99.4 15
< 0.1%
99.3 15
< 0.1%
99.2 9
< 0.1%
99.1 9
< 0.1%
99 5
 
< 0.1%

wind_speed
Real number (ℝ)

HIGH CORRELATION 

Distinct160
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.9719365
Minimum0
Maximum18.1
Zeros680
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size832.9 KiB
2023-07-13T19:26:20.607822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.4
Q12.9
median4.6
Q36.9
95-th percentile9.5
Maximum18.1
Range18.1
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.5674802
Coefficient of variation (CV)0.51639441
Kurtosis-0.55304258
Mean4.9719365
Median Absolute Deviation (MAD)1.9
Skewness0.39806157
Sum529978.6
Variance6.5919547
MonotonicityNot monotonic
2023-07-13T19:26:20.954484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.8 1779
 
1.7%
3 1771
 
1.7%
3.5 1728
 
1.6%
2.6 1722
 
1.6%
3.1 1717
 
1.6%
2.9 1690
 
1.6%
3.2 1689
 
1.6%
3.8 1679
 
1.6%
2.5 1670
 
1.6%
3.6 1648
 
1.5%
Other values (150) 89501
84.0%
ValueCountFrequency (%)
0 680
0.6%
0.1 195
 
0.2%
0.2 210
 
0.2%
0.3 212
 
0.2%
0.4 228
 
0.2%
0.5 231
 
0.2%
0.6 300
0.3%
0.7 289
0.3%
0.8 353
0.3%
0.9 386
0.4%
ValueCountFrequency (%)
18.1 1
 
< 0.1%
17.7 1
 
< 0.1%
17.2 1
 
< 0.1%
16.6 3
< 0.1%
15.8 2
< 0.1%
15.7 1
 
< 0.1%
15.6 3
< 0.1%
15.5 1
 
< 0.1%
15.4 1
 
< 0.1%
15.3 2
< 0.1%

wind_speed_of_gust
Real number (ℝ)

HIGH CORRELATION 

Distinct199
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.5248344
Minimum0
Maximum29.5
Zeros489
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size832.9 KiB
2023-07-13T19:26:21.327832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2.4
Q14.4
median6.9
Q310.4
95-th percentile14.2
Maximum29.5
Range29.5
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.7684093
Coefficient of variation (CV)0.50079631
Kurtosis-0.62278485
Mean7.5248344
Median Absolute Deviation (MAD)2.8
Skewness0.40649423
Sum802102.2
Variance14.200908
MonotonicityNot monotonic
2023-07-13T19:26:21.638593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.1 3067
 
2.9%
4.9 2989
 
2.8%
5.7 2806
 
2.6%
3.4 2760
 
2.6%
5.4 2687
 
2.5%
4.6 2640
 
2.5%
5.2 2631
 
2.5%
3.9 2619
 
2.5%
4.4 2600
 
2.4%
6.4 2526
 
2.4%
Other values (189) 79269
74.4%
ValueCountFrequency (%)
0 489
0.5%
0.4 23
 
< 0.1%
0.6 1
 
< 0.1%
0.7 1
 
< 0.1%
0.8 127
 
0.1%
1 10
 
< 0.1%
1.1 379
0.4%
1.2 4
 
< 0.1%
1.3 1
 
< 0.1%
1.4 442
0.4%
ValueCountFrequency (%)
29.5 1
< 0.1%
27.4 1
< 0.1%
25.7 2
< 0.1%
25.4 1
< 0.1%
25.2 1
< 0.1%
25 1
< 0.1%
23.2 1
< 0.1%
22.2 1
< 0.1%
21.9 2
< 0.1%
21.7 2
< 0.1%
Distinct648
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.725061
Minimum0
Maximum93.1
Zeros971
Zeros (%)0.9%
Negative0
Negative (%)0.0%
Memory size832.9 KiB
2023-07-13T19:26:21.963698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3.7
Q110.7
median14.9
Q317.9
95-th percentile25.7
Maximum93.1
Range93.1
Interquartile range (IQR)7.2

Descriptive statistics

Standard deviation6.8215562
Coefficient of variation (CV)0.46326167
Kurtosis6.3682179
Mean14.725061
Median Absolute Deviation (MAD)3.6
Skewness1.1539733
Sum1569603.1
Variance46.533628
MonotonicityNot monotonic
2023-07-13T19:26:22.310379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
16.9 1115
 
1.0%
17.1 1101
 
1.0%
16.6 1050
 
1.0%
17.6 1039
 
1.0%
16.4 1031
 
1.0%
16.1 1031
 
1.0%
17.4 1013
 
1.0%
16.8 1007
 
0.9%
0 971
 
0.9%
17.2 957
 
0.9%
Other values (638) 96279
90.3%
ValueCountFrequency (%)
0 971
0.9%
0.1 609
0.6%
0.2 243
 
0.2%
0.3 86
 
0.1%
0.4 79
 
0.1%
0.5 84
 
0.1%
0.6 68
 
0.1%
0.7 80
 
0.1%
0.8 83
 
0.1%
0.9 72
 
0.1%
ValueCountFrequency (%)
93.1 1
< 0.1%
84.6 1
< 0.1%
82.5 1
< 0.1%
81 1
< 0.1%
79.8 1
< 0.1%
79.7 1
< 0.1%
78.6 1
< 0.1%
78.1 1
< 0.1%
77.8 1
< 0.1%
77.7 1
< 0.1%

wind_from_direction
Real number (ℝ)

Distinct3598
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean188.45567
Minimum0
Maximum360
Zeros519
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size832.9 KiB
2023-07-13T19:26:22.617057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile8.865
Q1162.6
median209.3
Q3230.4
95-th percentile349.1
Maximum360
Range360
Interquartile range (IQR)67.8

Descriptive statistics

Standard deviation95.029482
Coefficient of variation (CV)0.50425377
Kurtosis-0.30019307
Mean188.45567
Median Absolute Deviation (MAD)26.8
Skewness-0.49972187
Sum20088244
Variance9030.6025
MonotonicityNot monotonic
2023-07-13T19:26:22.937054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 519
 
0.5%
211.7 175
 
0.2%
209.1 171
 
0.2%
214.2 171
 
0.2%
210.7 170
 
0.2%
210.3 169
 
0.2%
209.2 168
 
0.2%
209 164
 
0.2%
209.5 164
 
0.2%
218 163
 
0.2%
Other values (3588) 104560
98.1%
ValueCountFrequency (%)
0 519
0.5%
0.1 38
 
< 0.1%
0.2 53
 
< 0.1%
0.3 37
 
< 0.1%
0.4 41
 
< 0.1%
0.5 62
 
0.1%
0.6 52
 
< 0.1%
0.7 59
 
0.1%
0.8 44
 
< 0.1%
0.9 46
 
< 0.1%
ValueCountFrequency (%)
360 24
 
< 0.1%
359.9 69
0.1%
359.8 52
< 0.1%
359.7 49
< 0.1%
359.6 64
0.1%
359.5 43
< 0.1%
359.4 55
0.1%
359.3 53
< 0.1%
359.2 52
< 0.1%
359.1 46
< 0.1%

barometric_pressure
Real number (ℝ)

HIGH CORRELATION 

Distinct363
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1000.8149
Minimum982.5
Maximum1019
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size832.9 KiB
2023-07-13T19:26:23.630415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum982.5
5-th percentile989.7
Q1994.9
median1000.5
Q31007.2
95-th percentile1011.5
Maximum1019
Range36.5
Interquartile range (IQR)12.3

Descriptive statistics

Standard deviation7.1769299
Coefficient of variation (CV)0.0071710862
Kurtosis-1.0711052
Mean1000.8149
Median Absolute Deviation (MAD)6.1
Skewness-0.029299672
Sum1.0668086 × 108
Variance51.508323
MonotonicityNot monotonic
2023-07-13T19:26:23.992950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
997.4 723
 
0.7%
995.5 656
 
0.6%
996.6 630
 
0.6%
1007.7 618
 
0.6%
996.3 617
 
0.6%
1006.4 617
 
0.6%
1008.4 611
 
0.6%
1009.5 601
 
0.6%
1007.2 597
 
0.6%
1007.8 596
 
0.6%
Other values (353) 100328
94.1%
ValueCountFrequency (%)
982.5 2
 
< 0.1%
982.6 5
< 0.1%
982.7 1
 
< 0.1%
982.8 3
< 0.1%
982.9 3
< 0.1%
983 5
< 0.1%
983.2 4
< 0.1%
983.4 2
 
< 0.1%
983.5 1
 
< 0.1%
983.6 3
< 0.1%
ValueCountFrequency (%)
1019 5
< 0.1%
1018.9 4
< 0.1%
1018.8 3
 
< 0.1%
1018.7 1
 
< 0.1%
1018.5 3
 
< 0.1%
1018.4 2
 
< 0.1%
1018.3 6
< 0.1%
1018.2 9
< 0.1%
1018.1 7
< 0.1%
1018 6
< 0.1%

sensor_cleaning
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.1 MiB
0.0
106440 
1.0
 
153
0.1
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters319782
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
0.0 106440
99.9%
1.0 153
 
0.1%
0.1 1
 
< 0.1%

Length

2023-07-13T19:26:24.286280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-13T19:26:24.539638image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 106440
99.9%
1.0 153
 
0.1%
0.1 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 213034
66.6%
. 106594
33.3%
1 154
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 213188
66.7%
Other Punctuation 106594
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 213034
99.9%
1 154
 
0.1%
Other Punctuation
ValueCountFrequency (%)
. 106594
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 319782
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 213034
66.6%
. 106594
33.3%
1 154
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 319782
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 213034
66.6%
. 106594
33.3%
1 154
 
< 0.1%

comments
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing106594
Missing (%)100.0%
Memory size832.9 KiB

Interactions

2023-07-13T19:26:11.447213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-13T19:25:44.781022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-13T19:25:47.398771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-13T19:25:49.873230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-13T19:25:52.548397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-13T19:25:55.181540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-13T19:25:57.714624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-13T19:26:00.456624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-13T19:26:03.105466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-13T19:26:05.688628image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-13T19:26:08.247082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-13T19:26:11.691032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-13T19:25:45.056948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-13T19:25:47.615422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-13T19:25:50.277387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-13T19:25:52.789674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-13T19:25:55.431513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-13T19:25:57.939240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-13T19:26:00.697683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-13T19:26:03.347449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-13T19:26:05.914598image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-13T19:26:08.522382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-13T19:26:11.944871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-13T19:25:45.273571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-13T19:25:47.832076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-13T19:25:50.498529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-13T19:25:53.023082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-13T19:25:55.672730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-13T19:25:58.164614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-13T19:26:00.939020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-13T19:26:03.576253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-13T19:26:06.130641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-13T19:26:08.779701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-13T19:26:12.220076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-13T19:25:45.498925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-13T19:25:48.042989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-13T19:25:50.707175image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-13T19:25:53.248330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-13T19:25:55.890730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-13T19:25:58.381220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-13T19:26:01.172318image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-13T19:26:03.806782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-13T19:26:06.355239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-13T19:26:09.103635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-13T19:26:12.475089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-13T19:25:45.740217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-13T19:25:48.282068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-13T19:25:50.948492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-13T19:25:53.498292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-13T19:25:56.131417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-13T19:25:58.614554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-13T19:26:01.422301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-13T19:26:04.055842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-13T19:26:06.589205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-13T19:26:09.376233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-13T19:26:12.715374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-13T19:25:45.982190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-13T19:25:48.498652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-13T19:25:51.173147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-13T19:25:53.722933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-13T19:25:56.356664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-13T19:25:58.831727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-13T19:26:01.655625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-13T19:26:04.280740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-13T19:26:06.797220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-13T19:26:09.631917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-13T19:26:12.958791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-13T19:25:46.206833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-13T19:25:48.715333image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-13T19:25:51.389766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-13T19:25:53.956234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-13T19:25:56.572661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-13T19:25:59.289128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-13T19:26:01.881034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-13T19:26:04.505880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-13T19:26:07.021891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-13T19:26:09.870234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-13T19:26:13.216914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-13T19:25:46.442972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-13T19:25:48.948672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-13T19:25:51.631082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-13T19:25:54.206258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-13T19:25:56.805966image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-13T19:25:59.523110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-13T19:26:02.122886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-13T19:26:04.747372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-13T19:26:07.271840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-13T19:26:10.445112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-13T19:26:13.456930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-13T19:25:46.682240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-13T19:25:49.181958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-13T19:25:51.856377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-13T19:25:54.439490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-13T19:25:57.031363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-13T19:25:59.756430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-13T19:26:02.364790image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-13T19:26:04.980694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-13T19:26:07.513818image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-13T19:26:10.723793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-13T19:26:13.692031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-13T19:25:46.909551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-13T19:25:49.391412image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-13T19:25:52.065593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-13T19:25:54.680829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-13T19:25:57.247984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-13T19:25:59.972398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-13T19:26:02.596918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-13T19:26:05.197933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-13T19:26:07.755127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-13T19:26:10.955513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-13T19:26:13.932042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-13T19:25:47.148839image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-13T19:25:49.626001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-13T19:25:52.307259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-13T19:25:54.924796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-13T19:25:57.473300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-13T19:26:00.214376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-13T19:26:02.847526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-13T19:26:05.438653image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-13T19:26:08.005118image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-13T19:26:11.199251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-07-13T19:26:24.712977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ghi_pyrghi_rsidnidhiair_temperaturerelative_humiditywind_speedwind_speed_of_gustwind_from_direction_st_devwind_from_directionbarometric_pressuresensor_cleaning
ghi_pyr1.0000.9810.9330.9480.506-0.4290.1160.1870.461-0.177-0.0860.040
ghi_rsi0.9811.0000.9170.9670.478-0.4400.0910.1610.450-0.189-0.0580.041
dni0.9330.9171.0000.8400.404-0.4680.0310.0980.435-0.2040.0510.042
dhi0.9480.9670.8401.0000.485-0.3840.1410.2040.432-0.175-0.1210.033
air_temperature0.5060.4780.4040.4851.000-0.2940.4700.5270.300-0.018-0.7040.015
relative_humidity-0.429-0.440-0.468-0.384-0.2941.0000.2060.170-0.2050.083-0.2710.014
wind_speed0.1160.0910.0310.1410.4700.2061.0000.9820.2300.147-0.6270.000
wind_speed_of_gust0.1870.1610.0980.2040.5270.1700.9821.0000.3370.140-0.6500.000
wind_from_direction_st_dev0.4610.4500.4350.4320.300-0.2050.2300.3371.0000.007-0.0940.015
wind_from_direction-0.177-0.189-0.204-0.175-0.0180.0830.1470.1400.0071.000-0.1250.010
barometric_pressure-0.086-0.0580.051-0.121-0.704-0.271-0.627-0.650-0.094-0.1251.0000.005
sensor_cleaning0.0400.0410.0420.0330.0150.0140.0000.0000.0150.0100.0051.000

Missing values

2023-07-13T19:26:14.252070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-07-13T19:26:14.804502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

timeghi_pyrghi_rsidnidhiair_temperaturerelative_humiditywind_speedwind_speed_of_gustwind_from_direction_st_devwind_from_directionbarometric_pressuresensor_cleaningcomments
02015-04-21 18:3072.143.215.041.738.78.610.513.610.4248.5998.20.0NaN
12015-04-21 18:4037.322.70.022.738.48.210.513.810.1252.7998.20.0NaN
22015-04-21 18:504.04.30.04.338.08.38.912.212.4242.8998.50.0NaN
32015-04-21 19:000.01.20.01.237.78.88.812.712.6245.7998.61.0NaN
42015-04-21 19:100.00.00.00.037.49.38.411.412.8244.1998.61.0NaN
52015-04-21 19:200.00.00.00.037.19.97.511.214.3239.8998.70.0NaN
62015-04-21 19:300.00.00.00.036.711.17.611.415.4234.9998.90.0NaN
72015-04-21 19:400.00.00.00.036.113.38.112.216.7230.8998.90.0NaN
82015-04-21 19:500.00.00.00.035.813.08.111.916.2231.6999.10.0NaN
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